From ec005b50d59a337a73f34a1cb188b1bdb9d8156d Mon Sep 17 00:00:00 2001 From: Bo Zhou Date: Thu, 29 Nov 2018 16:19:42 +0800 Subject: [PATCH] add introduction about abstractions and features in README and logo (#31) * Update README.md * Update README.md * add diagram/logo * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md --- .github/PARL-logo.png | Bin 0 -> 28058 bytes .github/abstractions.png | Bin 0 -> 90724 bytes README.md | 66 +++++++++++++++++++++++++++++++++++++-- 3 files changed, 64 insertions(+), 2 deletions(-) create mode 100644 .github/PARL-logo.png create mode 100644 .github/abstractions.png diff --git a/.github/PARL-logo.png b/.github/PARL-logo.png new file mode 100644 index 0000000000000000000000000000000000000000..ed9d5d50ae2261deb9b4f811522ca79c5c3ae3cd GIT binary patch literal 28058 zcmZ5|1zc2H_ck#Sg2>PyIe@fu4GlAtbc1vw-Ccror!)c*f;1@7A>G~GNK1o$hkM`u z``!EUI}9`D?6dbid#&}XwVt(ya3uw)XHQ9lr0)L6|3|WO{?B6p56Jp> zg_Zpk8|#1j25uF6JPTEHvN8ike(YbEUGT4#|2p@daRgZ(2mi-p{(jQG&H|4r{8W(j zzsn~4RIY#m1iVoG-i6+yI)+E7T90yO%&x6wW#aqjZ7$NK6x3-fEffYm3#lWLWh)V zzGl>2@A~J_E9WMwbd#z<490E#rf(l;T7iWVUR*tTf1o zt=NAal8eLD!7B@4qY7l>yFxjSEf!(Ll&GPo>()oc{Y~?cRoO`5zEnJ@hY`Q#E5FSp z_ls??&+Ds2P3mXhL>7(M4E;B-U1}HkpXC7|2PU@xhV;Yd*RWU zRHg57tNMu!Fg+(O*2W}0@;B>nSA^7TVDbqo1b@1Y$~E`eyAMDN7tvaETIJ!LkfR!G z-)Ao7w8?IHKqE>9X2}qUtiY+ZF*EHoWOadPvKl2V)Np_iBNb#ltZ!k|i5b#tKEux& z{5-ei4|YzgDlwPH(I4rP52_wxSw^l?5I!j+eq=J-FK|&1>>tNHZg03MD4xnaJ;FI8 zvmWx(%WI;E1S6mq4o;mq>4KYm(I z$Yns-5CeI7#5#ZGOBAr%gjDhR7U{6$ZIw$XpU-#+yDE(F6NocvV36 z)6DB4PpXH(@EAFz%UjuhO$bIQgOuhTQ5*FV7Q)OEeO`_gYdJk=jNr*C{%fT8LTh6* z*!8}mb567fRYGiW1zW1FsJ0VUtbnRy?)<5TK3M_^!dnExw9Qp_&)^rmoQn8 zPz9d($Q;7343dq)gnBUBFZl;L-FdwSuTJ*+qLb74k*RLXJ#tETL-~XQ<%gxqw05LX z^2DKig)!hjaqz0^%=b&i_=%7)W~y#dt938i)r)#;FpY(V_mI4h`gzcMrjDy$7optx zJ59@umMFTZ?-{{nlK);7LnNd$mk6y5G?QneN>F!Ck8XaZ!DbJkZGzy<*4&mAE|Q>2 ze7qJC)tZo0Qc7IBsurBUN%YaN_WJIYqs{}u3Iy)eMIW&*3uQm~rx!a97NMfx*Hcb0 z1?4q(*ofeN9`uNTgZ%JCRg+=0s*~Fj`_*4sdWRgoTm@`Ah&>y*MnB++Nw1Z}l7GAd zTk^9Vat|J)DGa|I`b8yd%AF@E+$Rl&O3h;7G4J%p{L31`L{ZR9`x(R~EM5JTL=JK~ zHvSwCIlJ4|-@J~JAJwymhwxcQqp-`;g0Tawuq$V{@+H}%op|#kZ%4H4j&wZa`C<

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zO!iUNcwb5beQV66wMX7EW+=Q8{M!DH2mR)B(Ziq1{&-s(JN6qckN^L=$cTIGWs2a5 UM$`pkM}Ysy%e<2=l{E1Af1t<2pa1{> literal 0 HcmV?d00001 diff --git a/README.md b/README.md index 27bd133..5476837 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,64 @@ -# PARL -**Pa**ddlePaddle **R**einforcement **L**earning Framework +

+PARL +

+ +# Features +**Reproducible**. We provide algorithms that stably reproduce the result of many influential reinforcement learning algorithms + +**Large Scale**. Ability to support high performance parallelization of training with thousands of CPUs and multi-GPUs + +**Reusable**. Algorithms provided in repository could be directly adapted to a new task by defining a forward network and training mechanism will be built automatically. + +**Extensible**. Build new algorithms quickly by inheriting the abstract class in the framework. + + +# Abstractions +abstractions +PARL aims to build an agent for training algorithms to perform complex tasks. +The main abstractions introduced by PARL that are used to build an agent recursively are the following: + +### Model +`Model` is abstracted to construct the forward network which defines a policy network or critic network given state as input. + +### Algorithm +`Algorithm` describes the mechanism to update parameters in `Model` and often contains at least one model. + +### Agent +`Agent` is a data bridge between environment and algorithm. It is responsible for data I/O with outside and describes data preprocessing before feeding into the training process. + +Here is an example of building an agent with DQN algorithm for atari games. +```python +import parl +from parl.algorithms import DQN, DDQN + +class CriticModel(parl.Model): +""" define specific forward model for environment ...""" +""" +three steps to build an agent + 1. define a forward model which is critic_model is this example + 2. a. to build a DQN algorithm, just pass the critic_model to `DQN` + b. to build a DDQN algorithm, just replace DQN in following line with DDQN + 3. define the I/O part in AtariAgent so that it could update the algorithm based on the interactive data +""" + +critic_model = CriticModel(act_dim=2) +algorithm = DQN(critic_model) +agent = AtariAgent(aglrotihm) +``` + +# Install: +### Dependencies +- Python 2.7 or 3.5+. +- PaddlePaddle >=1.0 (We try to make our repository always compatible with newest version PaddlePaddle) + + +``` +pip install --upgrade git+https://github.com/PaddlePaddle/PARL.git +``` + +# Examples + +- DQN +- DDPG +- PPO +- Winning Solution for NIPS2018: AI for Prosthetics Challenge -- GitLab